Signal classification arises in a variety of applications. In signal classification, an input signal is received, and it is desired to determine to which of multiple classes the signal most likely belongs. For example, a simple classification task may be to automatically determine whether a received email is spam or is not spam. When an email is received, information about the email (e.g. the text of the email, the sender, an internet protocol address of the sender) may be processed using algorithms and models to classify the email as spam or as not spam.
Another example of signal classification relates to determining the identity of a speaker. A class may exist for each speaker of a set of speakers, and a model for each class may be created by processing speech samples of each speaker. To perform classification, a received speech signal may be compared to models for each class. The received signal may be assigned to a class based on a best match between the received signal and the class models.
In some instances, it may be desired to verify the identity of a speaker. A speaker may assert his identity (e.g., by providing a user name) and a speech sample. A model for the asserted identity may be obtained, and the received speech signal may be compared to the model. The classification task may be to determine whether the speech sample corresponds to the asserted identity.
In some instances, it may be desired to determine the identity of an unknown speaker using a speech sample of the unknown speaker. The speaker may be unknown, but it may be likely that the speaker is of a known set of speakers (e.g., the members of a household). The speech sample may be compared to models for each class (e.g., a model for each person in the household), and the classification task may be to determine which class best matches the speech sample or that no class matches the speech sample.
When performing signal classification, it is desired that the signal classification techniques have a low error rate, and the signal classification techniques described herein may have lower error rates than existing techniques.